Course Details

Courses Descriptions

📅 Month 1: Core Foundations – Linux, Networking & Infrastructure

🎯 Objective: Build rock-solid fundamentals required for production systems

🔧 Topics Covered

🐧 Linux internals (processes, memory, file systems, permissions, systemd)
📜 Advanced shell scripting (Bash, AWK, Sed, Cron jobs)
🌐 Networking fundamentals (TCP/IP, DNS, HTTP/HTTPS, Load Balancing)
🔐 OS-level security basics
🔑 SSH hardening & access control

🧪 Hands-On Labs

🖥️ Hardened Ubuntu Server setup
🌍 Secure NGINX web server deployment
🔁 Reverse proxy & load balancer configuration

☁️ Month 2: Cloud Fundamentals – AWS & Azure from Scratch

🎯 Objective: Understand cloud infrastructure at scale

🛠️ Technologies

🟧 AWS: EC2, VPC, IAM, S3, ALB, Auto Scaling
🟦 Azure: VM, VNets, NSG, Azure Storage
🌐 Cloud networking & identity design
💰 Cost optimization & tagging strategies

🏢 Industry Use Cases

🏗️ AWS infra setup for a TCS-style internal application
🏛️ Multi-tier architecture for an EY consulting workload

🐳 Month 3: Containerization & Kubernetes Engineering

🎯 Objective: Move from VM-based systems to container orchestration

⚙️ Technologies

📦 Docker internals & image optimization
☸️ Kubernetes architecture (API Server, Scheduler, etcd)
📊 Helm charts
🚦 Ingress controllers (NGINX, Traefik)
⚖️ Stateful vs Stateless workloads

🛒 Project

🛍️ Kubernetes-based microservices deployment for an
Amazon-like e-commerce backend

🔁 Month 4: CI/CD & DevOps Automation

🎯 Objective: Build automated delivery pipelines

🧰 Technologies

🔧 GitHub Actions, Jenkins, GitLab CI
🏗️ Infrastructure as Code (Terraform)
🛠️ Configuration management (Ansible)
🔄 Blue-Green & Canary deployments

🌍 Real-World Scenarios

🛒 CI/CD pipeline for Walmart-scale application releases
🏢 Automated infra provisioning for a PwC consulting client

🛡️ Month 5: DevSecOps – Security Embedded into Pipelines

🎯 Objective: Shift security left

🔐 Technologies & Practices

🧪 SAST, DAST, SCA
🔑 Secrets management (Vault)
📦 Container security (Trivy, Aqua)
☸️ Kubernetes RBAC & Network Policies
📜 Compliance automation

🧩 Project

📋 DevSecOps pipeline aligned with
KPMG audit & compliance standards

📊 Month 6: Observability, Reliability & AIOps Foundations

🎯 Objective: Operate systems intelligently at scale

📈 Technologies

📊 Prometheus & Grafana
📂 ELK Stack (Elasticsearch, Logstash, Kibana)
🧵 Distributed tracing (Jaeger)
🎯 SLA, SLO, Error Budgets
🤖 Introduction to AIOps

🚚 Use Case

⚡ Real-time monitoring for a
Blinkit-style logistics platform

🤖 Month 7: MLOps – Machine Learning in Production

🎯 Objective: Operationalize ML systems

🧠 Technologies

🔁 ML pipelines (training, validation, deployment)
📦 Model versioning (MLflow)
🏬 Feature stores
☸️ Kubernetes-based ML serving
🔄 CI/CD for ML models

📈 Project

🛍️ Demand forecasting model deployment
for Retail Analytics (Amazon/Walmart inspired)

🧠 Month 8: LLMOps – Managing Large Language Models

🎯 Objective: Deploy and manage LLM-based systems

🛠️ Technologies

🚀 LLM deployment pipelines
🧪 Model fine-tuning workflows
🗂️ Vector databases (Pinecone, FAISS)
✍️ Prompt engineering pipelines
🔌 API gateways for AI services

🏢 Enterprise Scenario

📚 Internal AI assistant for a
Deloitte-style consulting knowledge base

🏆 Month 9: Capstone Projects & Enterprise Simulation

🎯 Objective: Deliver production-grade systems end-to-end

🧩 Capstone Options (Choose One)

1️⃣ AI-Powered E-Commerce Platform 🛒
Amazon/Walmart Inspired
☁️ Cloud + ☸️ Kubernetes + 🔁 CI/CD + 🤖 AIOps + 💬 LLM Chatbot

2️⃣ Consulting Firm Cloud Platform 🏢
PwC/KPMG Inspired
🔐 Secure multi-tenant infra + DevSecOps + Compliance dashboards

3️⃣ Real-Time Logistics Intelligence Platform 🚚
Blinkit Inspired
📊 Observability + 📈 Predictive scaling + 🤖 ML-driven alerts

📦 Deliverables

📐 Architecture design documents
💻 GitHub repositories
📊 Monitoring dashboards
🔐 Security & cost reports
🚀 Production-grade deployment

🎓 Outcome & Career Readiness

By the end of the program, learners will be able to:

✅ Design & operate enterprise cloud platforms
✅ Build secure, scalable CI/CD pipelines
✅ Manage AI & ML workloads in production
✅ Work as Cloud Engineer, DevOps Engineer, SRE,
MLOps Engineer, Platform Engineer

🌟 Why This Program is Different

🔥 Starts from absolute fundamentals
🏁 Ends with real-world, enterprise-grade deployments
🧠 Covers DevOps + AI Operations, not just tools
🏢 Strong alignment with Big 4 consulting & product companies
🎯 Built for placement-backed, outcome-driven learning